from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-07 14:07:09.561444
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Mon, 07, Dec, 2020
Time: 14:07:13
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.3691
Nobs: 133.000 HQIC: -44.5302
Log likelihood: 1405.64 FPE: 2.07366e-20
AIC: -45.3249 Det(Omega_mle): 1.07989e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.524065 0.178773 2.931 0.003
L1.Burgenland 0.132471 0.085901 1.542 0.123
L1.Kärnten -0.298813 0.072188 -4.139 0.000
L1.Niederösterreich 0.098657 0.204952 0.481 0.630
L1.Oberösterreich 0.288531 0.171029 1.687 0.092
L1.Salzburg 0.158847 0.086734 1.831 0.067
L1.Steiermark 0.083773 0.122512 0.684 0.494
L1.Tirol 0.165033 0.081513 2.025 0.043
L1.Vorarlberg 0.011121 0.078753 0.141 0.888
L1.Wien -0.139389 0.163234 -0.854 0.393
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.542789 0.229197 2.368 0.018
L1.Burgenland -0.003470 0.110130 -0.032 0.975
L1.Kärnten 0.343661 0.092549 3.713 0.000
L1.Niederösterreich 0.129124 0.262760 0.491 0.623
L1.Oberösterreich -0.202011 0.219268 -0.921 0.357
L1.Salzburg 0.196070 0.111198 1.763 0.078
L1.Steiermark 0.228505 0.157068 1.455 0.146
L1.Tirol 0.140440 0.104504 1.344 0.179
L1.Vorarlberg 0.210980 0.100966 2.090 0.037
L1.Wien -0.566508 0.209276 -2.707 0.007
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.314163 0.078308 4.012 0.000
L1.Burgenland 0.104145 0.037627 2.768 0.006
L1.Kärnten -0.018534 0.031620 -0.586 0.558
L1.Niederösterreich 0.121092 0.089775 1.349 0.177
L1.Oberösterreich 0.275193 0.074916 3.673 0.000
L1.Salzburg -0.009003 0.037992 -0.237 0.813
L1.Steiermark -0.043641 0.053664 -0.813 0.416
L1.Tirol 0.091645 0.035705 2.567 0.010
L1.Vorarlberg 0.134332 0.034496 3.894 0.000
L1.Wien 0.038543 0.071501 0.539 0.590
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.183603 0.091192 2.013 0.044
L1.Burgenland 0.001681 0.043818 0.038 0.969
L1.Kärnten 0.033127 0.036823 0.900 0.368
L1.Niederösterreich 0.049323 0.104546 0.472 0.637
L1.Oberösterreich 0.370675 0.087241 4.249 0.000
L1.Salzburg 0.089451 0.044243 2.022 0.043
L1.Steiermark 0.207258 0.062493 3.316 0.001
L1.Tirol 0.033438 0.041580 0.804 0.421
L1.Vorarlberg 0.111504 0.040172 2.776 0.006
L1.Wien -0.082286 0.083265 -0.988 0.323
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.666449 0.195130 3.415 0.001
L1.Burgenland 0.062508 0.093761 0.667 0.505
L1.Kärnten -0.005415 0.078793 -0.069 0.945
L1.Niederösterreich -0.081458 0.223704 -0.364 0.716
L1.Oberösterreich 0.102379 0.186677 0.548 0.583
L1.Salzburg 0.042298 0.094670 0.447 0.655
L1.Steiermark 0.121654 0.133722 0.910 0.363
L1.Tirol 0.228737 0.088971 2.571 0.010
L1.Vorarlberg 0.037858 0.085959 0.440 0.660
L1.Wien -0.154179 0.178169 -0.865 0.387
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.227909 0.134194 1.698 0.089
L1.Burgenland -0.052005 0.064481 -0.807 0.420
L1.Kärnten -0.014091 0.054187 -0.260 0.795
L1.Niederösterreich 0.175668 0.153845 1.142 0.254
L1.Oberösterreich 0.393366 0.128381 3.064 0.002
L1.Salzburg -0.037013 0.065106 -0.569 0.570
L1.Steiermark -0.049867 0.091962 -0.542 0.588
L1.Tirol 0.197255 0.061187 3.224 0.001
L1.Vorarlberg 0.039617 0.059115 0.670 0.503
L1.Wien 0.131877 0.122530 1.076 0.282
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.245298 0.170830 1.436 0.151
L1.Burgenland 0.064825 0.082085 0.790 0.430
L1.Kärnten -0.077892 0.068981 -1.129 0.259
L1.Niederösterreich -0.108170 0.195846 -0.552 0.581
L1.Oberösterreich -0.095942 0.163430 -0.587 0.557
L1.Salzburg 0.012939 0.082880 0.156 0.876
L1.Steiermark 0.384485 0.117069 3.284 0.001
L1.Tirol 0.533595 0.077892 6.850 0.000
L1.Vorarlberg 0.228985 0.075254 3.043 0.002
L1.Wien -0.182121 0.155982 -1.168 0.243
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.109482 0.198821 0.551 0.582
L1.Burgenland 0.034369 0.095534 0.360 0.719
L1.Kärnten -0.084534 0.080283 -1.053 0.292
L1.Niederösterreich 0.153858 0.227935 0.675 0.500
L1.Oberösterreich 0.038898 0.190208 0.205 0.838
L1.Salzburg 0.219295 0.096460 2.273 0.023
L1.Steiermark 0.179480 0.136251 1.317 0.188
L1.Tirol 0.062497 0.090654 0.689 0.491
L1.Vorarlberg 0.029180 0.087584 0.333 0.739
L1.Wien 0.269288 0.181539 1.483 0.138
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.606699 0.109387 5.546 0.000
L1.Burgenland -0.017310 0.052561 -0.329 0.742
L1.Kärnten 0.002207 0.044170 0.050 0.960
L1.Niederösterreich -0.048875 0.125406 -0.390 0.697
L1.Oberösterreich 0.282387 0.104649 2.698 0.007
L1.Salzburg 0.009794 0.053071 0.185 0.854
L1.Steiermark 0.017941 0.074963 0.239 0.811
L1.Tirol 0.071424 0.049876 1.432 0.152
L1.Vorarlberg 0.180971 0.048187 3.756 0.000
L1.Wien -0.097219 0.099880 -0.973 0.330
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.101292 -0.042543 0.181998 0.239096 0.016166 0.068066 -0.133808 0.125576
Kärnten 0.101292 1.000000 -0.052899 0.180620 0.099260 -0.166211 0.189106 0.011156 0.266975
Niederösterreich -0.042543 -0.052899 1.000000 0.244610 0.059141 0.172674 0.086286 0.036930 0.362611
Oberösterreich 0.181998 0.180620 0.244610 1.000000 0.252856 0.268492 0.077112 0.064206 0.052488
Salzburg 0.239096 0.099260 0.059141 0.252856 1.000000 0.130726 0.042347 0.080917 -0.056431
Steiermark 0.016166 -0.166211 0.172674 0.268492 0.130726 1.000000 0.085424 0.071997 -0.178283
Tirol 0.068066 0.189106 0.086286 0.077112 0.042347 0.085424 1.000000 0.136149 0.100831
Vorarlberg -0.133808 0.011156 0.036930 0.064206 0.080917 0.071997 0.136149 1.000000 0.062685
Wien 0.125576 0.266975 0.362611 0.052488 -0.056431 -0.178283 0.100831 0.062685 1.000000